title: Quantifying the natural history of breast cancer.
abstract: BACKGROUND
  Natural history models of breast cancer progression provide an opportunity to evaluate and identify optimal screening scenarios. This paper describes a detailed Markov model characterising breast cancer tumour progression.
  
  
  METHODS
  Breast cancer is modelled by a 13-state continuous-time Markov model. The model differentiates between indolent and aggressive ductal carcinomas in situ tumours, and aggressive tumours of different sizes. We compared such aggressive cancers, that is, which are non-indolent, to those which are non-growing and regressing. Model input parameters and structure were informed by the 1978-1984 Ostergotland county breast screening randomised controlled trial. Overlaid on the natural history model is the effect of screening on diagnosis. Parameters were estimated using Bayesian methods. Markov chain Monte Carlo integration was used to sample the resulting posterior distribution.
  
  
  RESULTS
  The breast cancer incidence rate in the Ostergotland population was 21 (95% CI: 17-25) per 10 000 woman-years. Accounting for length-biased sampling, an estimated 91% (95% CI: 85-97%) of breast cancers were aggressive. Larger tumours, 21-50 mm, had an average sojourn of 6 years (95% CI: 3-16 years), whereas aggressive ductal carcinomas in situ took around half a month (95% CI: 0-1 month) to progress to the invasive ≤10 mm state.
  
  
  CONCLUSIONS
  These tumour progression rate estimates may facilitate future work analysing cost-effectiveness and quality-adjusted life years for various screening strategies.
authors.affiliation: Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Block MD3, Level 3, 16 Medical Drive, Singapore 117597, Singapore.
authors.forename: K H X
authors.initials: KH
authors.lastname: Tan
authors.forename: L
authors.initials: L
authors.lastname: Simonella
authors.forename: H L
authors.initials: HL
authors.lastname: Wee
authors.forename: A
authors.initials: A
authors.lastname: Roellin
authors.forename: Y-W
authors.initials: YW
authors.lastname: Lim
authors.forename: W-Y
authors.initials: WY
authors.lastname: Lim
authors.forename: K S
authors.initials: KS
authors.lastname: Chia
authors.forename: M
authors.initials: M
authors.lastname: Hartman
authors.forename: A R
authors.initials: AR
authors.lastname: Cook
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000453:epidemiology / Q000473:pathology*; D002278:Carcinoma in Situ / Q000000981:diagnostic imaging / Q000453:epidemiology / Q000473:pathology; D018270:Carcinoma, Ductal, Breast / Q000000981:diagnostic imaging / Q000453:epidemiology / Q000473:pathology; D018450:Disease Progression; D055088:Early Detection of Cancer / Q000379:methods / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008327:Mammography; D008390:Markov Chains; D008875:Middle Aged; D008954:Models, Biological; D009010:Monte Carlo Method; D016032:Randomized Controlled Trials as Topic; D015203:Reproducibility of Results; D013548:Sweden / Q000453:epidemiology